scholarly journals Fall Detection Based on Sequential Modeling of Radar Signal Time-Frequency Features

2013 ◽  
Author(s):  
Meng Wu ◽  
Xiaoxiao Dai ◽  
Yimin D. Zhang ◽  
Bradley Davidson ◽  
Moeness G. Amin ◽  
...  
Keyword(s):  
Fall Detection ◽  
Radar Signal ◽  
Time Frequency ◽  
Sequential Modeling ◽  
Frequency Features ◽  
Signal Time

scholarly journals Fall Detection Using Multiple Bioradars and Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5569 ◽  
Author(s):  
Lesya Anishchenko ◽  
Andrey Zhuravlev ◽  
Margarita Chizh
Keyword(s):  
Wavelet Transform ◽  
Input Data ◽  
Detection Method ◽  
Fall Detection ◽  
Modern Medicine ◽  
Radar Signal ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Representation ◽  
Life Threatening

A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%.

Classification of spot-welded joint strength using ultrasonic signal time-frequency features and PSO-SVM method

Ultrasonics ◽  
2019 ◽  
Vol 91 ◽  
pp. 161-169 ◽  
Author(s):  
Xiaokai Wang ◽  
Shanyue Guan ◽  
Lin Hua ◽  
Bin Wang ◽  
Ximing He
Keyword(s):  
Joint Strength ◽  
Welded Joint ◽  
Ultrasonic Signal ◽  
Time Frequency ◽  
Frequency Features ◽  
Signal Time ◽  
Spot Welded

scholarly journals Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

2021 ◽  
Vol 38 (5) ◽  
pp. 1541-1548
Author(s):  
Chang Liu ◽  
Ruslan Antypenko ◽  
Iryna Sushko ◽  
Oksana Zakharchenko ◽  
Ji Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Frequency Domain ◽  
Detection Algorithm ◽  
Radar Signal ◽  
Signal Identification ◽  
Time Frequency ◽  
Weight Calculation ◽  
Radar Signals ◽  
Frequency Features

Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.

scholarly journals Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2840
Author(s):  
Hubert Milczarek ◽  
Czesław Leśnik ◽  
Igor Djurović ◽  
Adam Kawalec
Keyword(s):  
Instantaneous Frequency ◽  
Early Warning Systems ◽  
Vital Role ◽  
Estimation Methods ◽  
Radar Signal ◽  
Nonlinear Frequency ◽  
Modulation Recognition ◽  
Electronic Warfare ◽  
Time Frequency ◽  
Radar Signals

Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.

scholarly journals Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors

2021 ◽  
Vol 13 (6) ◽  
pp. 1064
Author(s):  
Zhangjing Wang ◽  
Xianhan Miao ◽  
Zhen Huang ◽  
Haoran Luo
Keyword(s):  
Millimeter Wave ◽  
Autonomous Vehicles ◽  
Texture Features ◽  
Heterogeneous Data ◽  
Environmental Perception ◽  
Radar Signal ◽  
Time Frequency ◽  
Fusion Data ◽  
Wave Radar ◽  
Automation Equipment

The development of autonomous vehicles and unmanned aerial vehicles has led to a current research focus on improving the environmental perception of automation equipment. The unmanned platform detects its surroundings and then makes a decision based on environmental information. The major challenge of environmental perception is to detect and classify objects precisely; thus, it is necessary to perform fusion of different heterogeneous data to achieve complementary advantages. In this paper, a robust object detection and classification algorithm based on millimeter-wave (MMW) radar and camera fusion is proposed. The corresponding regions of interest (ROIs) are accurately calculated from the approximate position of the target detected by radar and cameras. A joint classification network is used to extract micro-Doppler features from the time-frequency spectrum and texture features from images in the ROIs. A fusion dataset between radar and camera is established using a fusion data acquisition platform and includes intersections, highways, roads, and playgrounds in schools during the day and at night. The traditional radar signal algorithm, the Faster R-CNN model and our proposed fusion network model, called RCF-Faster R-CNN, are evaluated in this dataset. The experimental results indicate that the mAP(mean Average Precision) of our network is up to 89.42% more accurate than the traditional radar signal algorithm and up to 32.76% higher than Faster R-CNN, especially in the environment of low light and strong electromagnetic clutter.

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